CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909099323555840 |
|---|---|
| author | Matsuzaki, Shigemichi Sugino, Takuma Tanaka, Kazuhito Sha, Zijun Nakaoka, Shintaro Yoshizawa, Shintaro Shintani, Kazuhiro |
| author_facet | Matsuzaki, Shigemichi Sugino, Takuma Tanaka, Kazuhito Sha, Zijun Nakaoka, Shintaro Yoshizawa, Shintaro Shintani, Kazuhiro |
| contents | This paper describes a multi-modal data association method for global localization using object-based maps and camera images. In global localization, or relocalization, using object-based maps, existing methods typically resort to matching all possible combinations of detected objects and landmarks with the same object category, followed by inlier extraction using RANSAC or brute-force search. This approach becomes infeasible as the number of landmarks increases due to the exponential growth of correspondence candidates. In this paper, we propose labeling landmarks with natural language descriptions and extracting correspondences based on conceptual similarity with image observations using a Vision Language Model (VLM). By leveraging detailed text information, our approach efficiently extracts correspondences compared to methods using only object categories. Through experiments, we demonstrate that the proposed method enables more accurate global localization with fewer iterations compared to baseline methods, exhibiting its efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps Matsuzaki, Shigemichi Sugino, Takuma Tanaka, Kazuhito Sha, Zijun Nakaoka, Shintaro Yoshizawa, Shintaro Shintani, Kazuhiro Computer Vision and Pattern Recognition Robotics This paper describes a multi-modal data association method for global localization using object-based maps and camera images. In global localization, or relocalization, using object-based maps, existing methods typically resort to matching all possible combinations of detected objects and landmarks with the same object category, followed by inlier extraction using RANSAC or brute-force search. This approach becomes infeasible as the number of landmarks increases due to the exponential growth of correspondence candidates. In this paper, we propose labeling landmarks with natural language descriptions and extracting correspondences based on conceptual similarity with image observations using a Vision Language Model (VLM). By leveraging detailed text information, our approach efficiently extracts correspondences compared to methods using only object categories. Through experiments, we demonstrate that the proposed method enables more accurate global localization with fewer iterations compared to baseline methods, exhibiting its efficiency. |
| title | CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2402.06092 |